MétaCan
Menu
Back to cohort
Record W3002483259 · doi:10.1016/j.accinf.2019.100443

Social media capital: Conceptualizing the nature, acquisition, and expenditure of social media-based organizational resources

2020· article· en· W3002483259 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Accounting Information Systems · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork University
Fundersnot available
KeywordsSocial mediaExploitSocial capitalKnowledge managementResource (disambiguation)Public relationsBusinessSociologyComputer sciencePolitical scienceWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

The near-universal organizational participation in social media is predicated on the belief there are some tangible or intangible new resources to be had through tweeting, pinning, posting, friending, and sharing. We argue the linchpin of any payoff from engagement in social media is a special form of social capital we refer to as social media capital, and offer a conceptual framework for understanding its nature, acquisition, and expenditure. This paper contributes to existing literature by elaborating a new type of organizational resource and then synthesizing and extending research on the processes through which organizations can translate social media efforts into meaningful organizational outcomes. Understanding this causal chain is critical not only for measuring the return on investment from social media use but also for developing accounting information systems that are both adaptable to social resources and better able to exploit the data analytic and forecasting capabilities of real-time social media data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.277
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it